In [1]}, the authors performed active learning on Bitcoin transaction data to detect money laundering. The authors assumed to have limited ground truth about the transaction data. Active learning is applied to data clustered through unsupervised learning. The results obtained have performance similar to that of the supervised ML model with fully labeled data. The benchmark results in aforementioned case were obtained from [2]}. In [2]}, the authors contributed to the elliptic data set, claimed as the largest labeled Bitcoin transactions data set till their submission. For the prediction of illicit transactions, the Random Forest method performed best among the used supervised ML models.
